path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
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|---|---|---|---|
90130223/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import roc_auc_score
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 581
data = pd.read_csv('/kaggle/input/mushroom-classification/mushrooms.csv')
print(data.shape)
data.head() | code |
90130223/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import roc_auc_score
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 581
data = pd.read_csv('/kaggle/input/mushroom-classification/mushrooms.csv')
data.isna().any()
mapping = {'e': 1, 'p': 0}
data.rename({'class': 'edible'}, axis=1, inplace=True)
data['edible'] = data['edible'].replace(mapping)
data = data.astype('category')
data.dtypes
data.head() | code |
90130223/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import roc_auc_score
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 581
data = pd.read_csv('/kaggle/input/mushroom-classification/mushrooms.csv')
data.isna().any()
mapping = {'e': 1, 'p': 0}
data.rename({'class': 'edible'}, axis=1, inplace=True)
data['edible'] = data['edible'].replace(mapping)
data = data.astype('category')
data.dtypes
data['edible'].value_counts(normalize='True') | code |
90130223/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import roc_auc_score
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 581
data = pd.read_csv('/kaggle/input/mushroom-classification/mushrooms.csv')
data.isna().any()
mapping = {'e': 1, 'p': 0}
data.rename({'class': 'edible'}, axis=1, inplace=True)
data['edible'] = data['edible'].replace(mapping)
data = data.astype('category')
data.dtypes | code |
90130223/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import roc_auc_score
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 581
data = pd.read_csv('/kaggle/input/mushroom-classification/mushrooms.csv')
data.isna().any()
mapping = {'e': 1, 'p': 0}
data.rename({'class': 'edible'}, axis=1, inplace=True)
data['edible'] = data['edible'].replace(mapping)
data = data.astype('category')
data.dtypes
sum = 0
for n in data.nunique():
sum += n
sum = sum - data.shape[1]
data = pd.get_dummies(data, drop_first=True)
print(data.shape)
data.head() | code |
90130223/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import roc_auc_score
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 581
data = pd.read_csv('/kaggle/input/mushroom-classification/mushrooms.csv')
data.isna().any() | code |
105205397/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/iris/Iris.csv')
x = data.drop(['Species', 'Id'], axis=1)
y = data.Species
model = RandomForestClassifier()
x_train, x_val, y_train, y_val = train_test_split(x, y)
model.fit(x_train, y_train)
pred = model.predict(x_val)
print(classification_report(y_val, pred)) | code |
105205397/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/iris/Iris.csv')
x = data.drop(['Species', 'Id'], axis=1)
y = data.Species
sns.countplot(y) | code |
105205397/cell_19 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/iris/Iris.csv')
x = data.drop(['Species', 'Id'], axis=1)
y = data.Species
model = RandomForestClassifier()
x_train, x_val, y_train, y_val = train_test_split(x, y)
model.fit(x_train, y_train)
pred = model.predict(x_val)
accuracy_score(y_val, pred) | code |
105205397/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
105205397/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/iris/Iris.csv')
data.info() | code |
105205397/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/iris/Iris.csv')
data.describe() | code |
105205397/cell_16 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/iris/Iris.csv')
x = data.drop(['Species', 'Id'], axis=1)
y = data.Species
model = RandomForestClassifier()
x_train, x_val, y_train, y_val = train_test_split(x, y)
model.fit(x_train, y_train) | code |
105205397/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/iris/Iris.csv')
x = data.drop(['Species', 'Id'], axis=1)
y = data.Species
sns.heatmap(data.corr(), annot=True) | code |
105205397/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/iris/Iris.csv')
data.head() | code |
2013560/cell_42 | [
"text_html_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
labels = ('Cherbourg', 'Queenstown', 'Southampton')
sizes = [sum(train['Embarked'] == 'C'), sum(train['Embarked'] == 'Q'), sum(train['Embarked'] == 'S')]
colors = ['yellow', 'aqua', 'lime']
plt.axis('equal')
def survival_stacked_bar(variable):
Died = train[train['Survived'] == 0][variable].value_counts() / len(train['Survived'] == 0)
Survived = train[train['Survived'] == 1][variable].value_counts() / len(train['Survived'] == 1)
data = pd.DataFrame([Died, Survived])
data.index = ['Did not survived', 'Survived']
return
f,ax = plt.subplots(figsize=(10, 10))
sns.heatmap(train.corr(), annot=True, linewidths=0.5, fmt= '.2f',ax=ax)
traintestdata = pd.concat([train, test])
traintestdata.shape
traintestdata['Title'] = traintestdata.Name.map(lambda name: name.split(',')[1].split('.')[0].strip())
(train.Title.unique(), test.Title.unique())
title_map = {'Capt': 'Officer', 'Col': 'Officer', 'Major': 'Officer', 'Jonkheer': 'Royalty', 'Don': 'Royalty', 'Sir': 'Royalty', 'Dr': 'Officer', 'Rev': 'Officer', 'the Countess': 'Royalty', 'Dona': 'Royalty', 'Mme': 'Mrs', 'Mlle': 'Miss', 'Ms': 'Mrs', 'Mr': 'Mr', 'Mrs': 'Mrs', 'Miss': 'Miss', 'Master': 'Master', 'Lady': 'Royalty'}
traintestdata['Title'] = traintestdata['Title'].map(title_map)
(train.Title.unique(), test.Title.unique()) | code |
2013560/cell_21 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
def survival_stacked_bar(variable):
Died = train[train['Survived'] == 0][variable].value_counts() / len(train['Survived'] == 0)
Survived = train[train['Survived'] == 1][variable].value_counts() / len(train['Survived'] == 1)
data = pd.DataFrame([Died, Survived])
data.index = ['Did not survived', 'Survived']
return
survival_stacked_bar('Pclass') | code |
2013560/cell_25 | [
"text_html_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
def survival_stacked_bar(variable):
Died = train[train['Survived'] == 0][variable].value_counts() / len(train['Survived'] == 0)
Survived = train[train['Survived'] == 1][variable].value_counts() / len(train['Survived'] == 1)
data = pd.DataFrame([Died, Survived])
data.index = ['Did not survived', 'Survived']
return
survival_stacked_bar('SibSp') | code |
2013560/cell_34 | [
"text_html_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
labels = ('Cherbourg', 'Queenstown', 'Southampton')
sizes = [sum(train['Embarked'] == 'C'), sum(train['Embarked'] == 'Q'), sum(train['Embarked'] == 'S')]
colors = ['yellow', 'aqua', 'lime']
plt.axis('equal')
def survival_stacked_bar(variable):
Died = train[train['Survived'] == 0][variable].value_counts() / len(train['Survived'] == 0)
Survived = train[train['Survived'] == 1][variable].value_counts() / len(train['Survived'] == 1)
data = pd.DataFrame([Died, Survived])
data.index = ['Did not survived', 'Survived']
return
f,ax = plt.subplots(figsize=(10, 10))
sns.heatmap(train.corr(), annot=True, linewidths=0.5, fmt= '.2f',ax=ax)
traintestdata = pd.concat([train, test])
traintestdata.shape | code |
2013560/cell_23 | [
"text_html_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
def survival_stacked_bar(variable):
Died = train[train['Survived'] == 0][variable].value_counts() / len(train['Survived'] == 0)
Survived = train[train['Survived'] == 1][variable].value_counts() / len(train['Survived'] == 1)
data = pd.DataFrame([Died, Survived])
data.index = ['Did not survived', 'Survived']
return
survival_stacked_bar('Embarked') | code |
2013560/cell_30 | [
"text_html_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
labels = ('Cherbourg', 'Queenstown', 'Southampton')
sizes = [sum(train['Embarked'] == 'C'), sum(train['Embarked'] == 'Q'), sum(train['Embarked'] == 'S')]
colors = ['yellow', 'aqua', 'lime']
plt.axis('equal')
f, ax = plt.subplots(figsize=(10, 10))
sns.heatmap(train.corr(), annot=True, linewidths=0.5, fmt='.2f', ax=ax) | code |
2013560/cell_44 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
labels = ('Cherbourg', 'Queenstown', 'Southampton')
sizes = [sum(train['Embarked'] == 'C'), sum(train['Embarked'] == 'Q'), sum(train['Embarked'] == 'S')]
colors = ['yellow', 'aqua', 'lime']
plt.axis('equal')
def survival_stacked_bar(variable):
Died = train[train['Survived'] == 0][variable].value_counts() / len(train['Survived'] == 0)
Survived = train[train['Survived'] == 1][variable].value_counts() / len(train['Survived'] == 1)
data = pd.DataFrame([Died, Survived])
data.index = ['Did not survived', 'Survived']
return
f,ax = plt.subplots(figsize=(10, 10))
sns.heatmap(train.corr(), annot=True, linewidths=0.5, fmt= '.2f',ax=ax)
traintestdata = pd.concat([train, test])
traintestdata.shape
traintestdata['Title'] = traintestdata.Name.map(lambda name: name.split(',')[1].split('.')[0].strip())
(train.Title.unique(), test.Title.unique())
title_map = {'Capt': 'Officer', 'Col': 'Officer', 'Major': 'Officer', 'Jonkheer': 'Royalty', 'Don': 'Royalty', 'Sir': 'Royalty', 'Dr': 'Officer', 'Rev': 'Officer', 'the Countess': 'Royalty', 'Dona': 'Royalty', 'Mme': 'Mrs', 'Mlle': 'Miss', 'Ms': 'Mrs', 'Mr': 'Mr', 'Mrs': 'Mrs', 'Miss': 'Miss', 'Master': 'Master', 'Lady': 'Royalty'}
traintestdata['Title'] = traintestdata['Title'].map(title_map)
(train.Title.unique(), test.Title.unique())
for i in train.columns:
print(i + ': ' + str(sum(train[i].isnull())) + ' missing values') | code |
2013560/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
gender_submission.head() | code |
2013560/cell_40 | [
"text_html_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
labels = ('Cherbourg', 'Queenstown', 'Southampton')
sizes = [sum(train['Embarked'] == 'C'), sum(train['Embarked'] == 'Q'), sum(train['Embarked'] == 'S')]
colors = ['yellow', 'aqua', 'lime']
plt.axis('equal')
def survival_stacked_bar(variable):
Died = train[train['Survived'] == 0][variable].value_counts() / len(train['Survived'] == 0)
Survived = train[train['Survived'] == 1][variable].value_counts() / len(train['Survived'] == 1)
data = pd.DataFrame([Died, Survived])
data.index = ['Did not survived', 'Survived']
return
f,ax = plt.subplots(figsize=(10, 10))
sns.heatmap(train.corr(), annot=True, linewidths=0.5, fmt= '.2f',ax=ax)
traintestdata = pd.concat([train, test])
traintestdata.shape
traintestdata['Title'] = traintestdata.Name.map(lambda name: name.split(',')[1].split('.')[0].strip())
(train.Title.unique(), test.Title.unique()) | code |
2013560/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd
pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.set_option('display.notebook_repr_html', True)
import seaborn as sns
sns.set(style='whitegrid')
from sklearn.linear_model import LinearRegression
import statsmodels.formula.api as sm
from sklearn.cross_validation import train_test_split | code |
2013560/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
train.info() | code |
2013560/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
def survival_stacked_bar(variable):
Died = train[train['Survived'] == 0][variable].value_counts() / len(train['Survived'] == 0)
Survived = train[train['Survived'] == 1][variable].value_counts() / len(train['Survived'] == 1)
data = pd.DataFrame([Died, Survived])
data.index = ['Did not survived', 'Survived']
return
survival_stacked_bar('Sex') | code |
2013560/cell_45 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
labels = ('Cherbourg', 'Queenstown', 'Southampton')
sizes = [sum(train['Embarked'] == 'C'), sum(train['Embarked'] == 'Q'), sum(train['Embarked'] == 'S')]
colors = ['yellow', 'aqua', 'lime']
plt.axis('equal')
def survival_stacked_bar(variable):
Died = train[train['Survived'] == 0][variable].value_counts() / len(train['Survived'] == 0)
Survived = train[train['Survived'] == 1][variable].value_counts() / len(train['Survived'] == 1)
data = pd.DataFrame([Died, Survived])
data.index = ['Did not survived', 'Survived']
return
f,ax = plt.subplots(figsize=(10, 10))
sns.heatmap(train.corr(), annot=True, linewidths=0.5, fmt= '.2f',ax=ax)
traintestdata = pd.concat([train, test])
traintestdata.shape
traintestdata['Title'] = traintestdata.Name.map(lambda name: name.split(',')[1].split('.')[0].strip())
(train.Title.unique(), test.Title.unique())
title_map = {'Capt': 'Officer', 'Col': 'Officer', 'Major': 'Officer', 'Jonkheer': 'Royalty', 'Don': 'Royalty', 'Sir': 'Royalty', 'Dr': 'Officer', 'Rev': 'Officer', 'the Countess': 'Royalty', 'Dona': 'Royalty', 'Mme': 'Mrs', 'Mlle': 'Miss', 'Ms': 'Mrs', 'Mr': 'Mr', 'Mrs': 'Mrs', 'Miss': 'Miss', 'Master': 'Master', 'Lady': 'Royalty'}
traintestdata['Title'] = traintestdata['Title'].map(title_map)
(train.Title.unique(), test.Title.unique())
for i in test.columns:
print(i + ': ' + str(sum(test[i].isnull())) + ' missing values') | code |
2013560/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
train['Sex'].value_counts().plot(kind='bar') | code |
2013560/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
labels = ('Cherbourg', 'Queenstown', 'Southampton')
sizes = [sum(train['Embarked'] == 'C'), sum(train['Embarked'] == 'Q'), sum(train['Embarked'] == 'S')]
colors = ['yellow', 'aqua', 'lime']
plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90)
plt.axis('equal')
plt.show() | code |
2013560/cell_38 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
labels = ('Cherbourg', 'Queenstown', 'Southampton')
sizes = [sum(train['Embarked'] == 'C'), sum(train['Embarked'] == 'Q'), sum(train['Embarked'] == 'S')]
colors = ['yellow', 'aqua', 'lime']
plt.axis('equal')
def survival_stacked_bar(variable):
Died = train[train['Survived'] == 0][variable].value_counts() / len(train['Survived'] == 0)
Survived = train[train['Survived'] == 1][variable].value_counts() / len(train['Survived'] == 1)
data = pd.DataFrame([Died, Survived])
data.index = ['Did not survived', 'Survived']
return
f,ax = plt.subplots(figsize=(10, 10))
sns.heatmap(train.corr(), annot=True, linewidths=0.5, fmt= '.2f',ax=ax)
traintestdata = pd.concat([train, test])
traintestdata.shape
embark_map = {'S': 1, 'C': 2, 'Q': 3}
traintestdata['Embarked'] = traintestdata['Embarked'].map(embark_map)
survival_stacked_bar('Embarked') | code |
2013560/cell_47 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
labels = ('Cherbourg', 'Queenstown', 'Southampton')
sizes = [sum(train['Embarked'] == 'C'), sum(train['Embarked'] == 'Q'), sum(train['Embarked'] == 'S')]
colors = ['yellow', 'aqua', 'lime']
plt.axis('equal')
def survival_stacked_bar(variable):
Died = train[train['Survived'] == 0][variable].value_counts() / len(train['Survived'] == 0)
Survived = train[train['Survived'] == 1][variable].value_counts() / len(train['Survived'] == 1)
data = pd.DataFrame([Died, Survived])
data.index = ['Did not survived', 'Survived']
return
f,ax = plt.subplots(figsize=(10, 10))
sns.heatmap(train.corr(), annot=True, linewidths=0.5, fmt= '.2f',ax=ax)
traintestdata = pd.concat([train, test])
traintestdata.shape
traintestdata['Title'] = traintestdata.Name.map(lambda name: name.split(',')[1].split('.')[0].strip())
(train.Title.unique(), test.Title.unique())
title_map = {'Capt': 'Officer', 'Col': 'Officer', 'Major': 'Officer', 'Jonkheer': 'Royalty', 'Don': 'Royalty', 'Sir': 'Royalty', 'Dr': 'Officer', 'Rev': 'Officer', 'the Countess': 'Royalty', 'Dona': 'Royalty', 'Mme': 'Mrs', 'Mlle': 'Miss', 'Ms': 'Mrs', 'Mr': 'Mr', 'Mrs': 'Mrs', 'Miss': 'Miss', 'Master': 'Master', 'Lady': 'Royalty'}
traintestdata['Title'] = traintestdata['Title'].map(title_map)
(train.Title.unique(), test.Title.unique())
train_set_1 = train.groupby(['Sex', 'Title', 'Pclass', 'Parch'])
train_set_1_median = train_set_1.median()
train_set_1 | code |
2013560/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
train['Age'].hist(width=6) | code |
2013560/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape) | code |
2013560/cell_27 | [
"text_html_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
def survival_stacked_bar(variable):
Died = train[train['Survived'] == 0][variable].value_counts() / len(train['Survived'] == 0)
Survived = train[train['Survived'] == 1][variable].value_counts() / len(train['Survived'] == 1)
data = pd.DataFrame([Died, Survived])
data.index = ['Did not survived', 'Survived']
return
survival_stacked_bar('Parch') | code |
2013560/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
test.info() | code |
2013560/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
train.head() | code |
2013560/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
labels = ('Cherbourg', 'Queenstown', 'Southampton')
sizes = [sum(train['Embarked'] == 'C'), sum(train['Embarked'] == 'Q'), sum(train['Embarked'] == 'S')]
colors = ['yellow', 'aqua', 'lime']
plt.axis('equal')
def survival_stacked_bar(variable):
Died = train[train['Survived'] == 0][variable].value_counts() / len(train['Survived'] == 0)
Survived = train[train['Survived'] == 1][variable].value_counts() / len(train['Survived'] == 1)
data = pd.DataFrame([Died, Survived])
data.index = ['Did not survived', 'Survived']
return
f,ax = plt.subplots(figsize=(10, 10))
sns.heatmap(train.corr(), annot=True, linewidths=0.5, fmt= '.2f',ax=ax)
traintestdata = pd.concat([train, test])
traintestdata.shape
sex_map = {'male': 1, 'female': 0}
traintestdata['Sex'] = traintestdata['Sex'].map(sex_map)
survival_stacked_bar('Sex') | code |
2022050/cell_13 | [
"text_html_output_1.png"
] | from pandas.io.json import json_normalize
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.concat(deserialize_cr, ignore_index=True)
df_cr.columns = ['Left Clan', 'Left Deck', 'Left Player', 'Left Trophy', 'Right Clan', 'Right Deck', 'Right Player', 'right Trophy', 'Result', 'Time', 'Type']
Left_Troops = list(np.hstack([[x[0] for x in left_deck] for left_deck in df_cr['Left Deck']]))
Right_Troops = list(np.hstack([[x[0] for x in right_deck] for right_deck in df_cr['Right Deck']]))
distinct_troops = set(np.hstack([Left_Troops, Right_Troops]))
len(distinct_troops)
RightArmy_colNames = np.hstack([['Right Troop ' + str(i + 1) for i in range(8)], ['Right Troop Count ' + str(i + 1) for i in range(8)]])
LeftArmy_colNames = np.hstack([['Left Troop ' + str(i + 1) for i in range(8)], ['Left Troop Count ' + str(i + 1) for i in range(8)]])
RightArmy = pd.DataFrame(data=[np.hstack([[army[0] for army in x], [army[1] for army in x]]) for x in df_cr['Right Deck']], columns=RightArmy_colNames)
LeftArmy = pd.DataFrame(data=[np.hstack([[army[0] for army in x], [army[1] for army in x]]) for x in df_cr['Left Deck']], columns=LeftArmy_colNames)
finalCR_data = pd.concat([df_cr, LeftArmy, RightArmy], axis=1, join='inner')
finalCR_data.head() | code |
2022050/cell_9 | [
"text_html_output_1.png"
] | from pandas.io.json import json_normalize
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.concat(deserialize_cr, ignore_index=True)
df_cr.columns = ['Left Clan', 'Left Deck', 'Left Player', 'Left Trophy', 'Right Clan', 'Right Deck', 'Right Player', 'right Trophy', 'Result', 'Time', 'Type']
LD = [len(left_deck) for left_deck in df_cr['Left Deck']]
RD = [len(right_deck) for right_deck in df_cr['Right Deck']]
(set(LD), set(RD)) | code |
2022050/cell_23 | [
"text_html_output_1.png"
] | from pandas.io.json import json_normalize
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.concat(deserialize_cr, ignore_index=True)
df_cr.columns = ['Left Clan', 'Left Deck', 'Left Player', 'Left Trophy', 'Right Clan', 'Right Deck', 'Right Player', 'right Trophy', 'Result', 'Time', 'Type']
Left_Troops = list(np.hstack([[x[0] for x in left_deck] for left_deck in df_cr['Left Deck']]))
Right_Troops = list(np.hstack([[x[0] for x in right_deck] for right_deck in df_cr['Right Deck']]))
distinct_troops = set(np.hstack([Left_Troops, Right_Troops]))
len(distinct_troops)
RightArmy_colNames = np.hstack([['Right Troop ' + str(i + 1) for i in range(8)], ['Right Troop Count ' + str(i + 1) for i in range(8)]])
LeftArmy_colNames = np.hstack([['Left Troop ' + str(i + 1) for i in range(8)], ['Left Troop Count ' + str(i + 1) for i in range(8)]])
RightArmy = pd.DataFrame(data=[np.hstack([[army[0] for army in x], [army[1] for army in x]]) for x in df_cr['Right Deck']], columns=RightArmy_colNames)
LeftArmy = pd.DataFrame(data=[np.hstack([[army[0] for army in x], [army[1] for army in x]]) for x in df_cr['Left Deck']], columns=LeftArmy_colNames)
finalCR_data = pd.concat([df_cr, LeftArmy, RightArmy], axis=1, join='inner')
finalCR_data[(finalCR_data['Battle Result'] == 'Right') & (finalCR_data['Right Stars Won'] == '3')][['Right Troop 1', 'Right Troop 2', 'Right Troop 3', 'Right Troop 4', 'Right Troop 5', 'Right Troop 6', 'Right Troop 7', 'Right Troop 8', 'Result']].groupby(['Right Troop 1', 'Right Troop 2', 'Right Troop 3', 'Right Troop 4', 'Right Troop 5', 'Right Troop 6', 'Right Troop 7', 'Right Troop 8']).count().sort_values(by='Result', ascending=False).head(5) | code |
2022050/cell_20 | [
"text_plain_output_1.png"
] | from pandas.io.json import json_normalize
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.concat(deserialize_cr, ignore_index=True)
df_cr.columns = ['Left Clan', 'Left Deck', 'Left Player', 'Left Trophy', 'Right Clan', 'Right Deck', 'Right Player', 'right Trophy', 'Result', 'Time', 'Type']
Left_Troops = list(np.hstack([[x[0] for x in left_deck] for left_deck in df_cr['Left Deck']]))
Right_Troops = list(np.hstack([[x[0] for x in right_deck] for right_deck in df_cr['Right Deck']]))
distinct_troops = set(np.hstack([Left_Troops, Right_Troops]))
len(distinct_troops)
RightArmy_colNames = np.hstack([['Right Troop ' + str(i + 1) for i in range(8)], ['Right Troop Count ' + str(i + 1) for i in range(8)]])
LeftArmy_colNames = np.hstack([['Left Troop ' + str(i + 1) for i in range(8)], ['Left Troop Count ' + str(i + 1) for i in range(8)]])
RightArmy = pd.DataFrame(data=[np.hstack([[army[0] for army in x], [army[1] for army in x]]) for x in df_cr['Right Deck']], columns=RightArmy_colNames)
LeftArmy = pd.DataFrame(data=[np.hstack([[army[0] for army in x], [army[1] for army in x]]) for x in df_cr['Left Deck']], columns=LeftArmy_colNames)
finalCR_data = pd.concat([df_cr, LeftArmy, RightArmy], axis=1, join='inner')
finalCR_data[['Result', 'Battle Result']].groupby('Battle Result').count().apply(lambda x: x / x.sum() * 100) | code |
2022050/cell_11 | [
"text_plain_output_1.png"
] | from pandas.io.json import json_normalize
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.concat(deserialize_cr, ignore_index=True)
df_cr.columns = ['Left Clan', 'Left Deck', 'Left Player', 'Left Trophy', 'Right Clan', 'Right Deck', 'Right Player', 'right Trophy', 'Result', 'Time', 'Type']
Left_Troops = list(np.hstack([[x[0] for x in left_deck] for left_deck in df_cr['Left Deck']]))
Right_Troops = list(np.hstack([[x[0] for x in right_deck] for right_deck in df_cr['Right Deck']]))
distinct_troops = set(np.hstack([Left_Troops, Right_Troops]))
len(distinct_troops) | code |
2022050/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2022050/cell_7 | [
"text_html_output_1.png"
] | from pandas.io.json import json_normalize
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.concat(deserialize_cr, ignore_index=True)
df_cr.columns = ['Left Clan', 'Left Deck', 'Left Player', 'Left Trophy', 'Right Clan', 'Right Deck', 'Right Player', 'right Trophy', 'Result', 'Time', 'Type']
df_cr.head() | code |
2022050/cell_16 | [
"text_plain_output_1.png"
] | from pandas.io.json import json_normalize
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.concat(deserialize_cr, ignore_index=True)
df_cr.columns = ['Left Clan', 'Left Deck', 'Left Player', 'Left Trophy', 'Right Clan', 'Right Deck', 'Right Player', 'right Trophy', 'Result', 'Time', 'Type']
Left_Troops = list(np.hstack([[x[0] for x in left_deck] for left_deck in df_cr['Left Deck']]))
Right_Troops = list(np.hstack([[x[0] for x in right_deck] for right_deck in df_cr['Right Deck']]))
distinct_troops = set(np.hstack([Left_Troops, Right_Troops]))
len(distinct_troops)
RightArmy_colNames = np.hstack([['Right Troop ' + str(i + 1) for i in range(8)], ['Right Troop Count ' + str(i + 1) for i in range(8)]])
LeftArmy_colNames = np.hstack([['Left Troop ' + str(i + 1) for i in range(8)], ['Left Troop Count ' + str(i + 1) for i in range(8)]])
RightArmy = pd.DataFrame(data=[np.hstack([[army[0] for army in x], [army[1] for army in x]]) for x in df_cr['Right Deck']], columns=RightArmy_colNames)
LeftArmy = pd.DataFrame(data=[np.hstack([[army[0] for army in x], [army[1] for army in x]]) for x in df_cr['Left Deck']], columns=LeftArmy_colNames)
finalCR_data = pd.concat([df_cr, LeftArmy, RightArmy], axis=1, join='inner')
finalCR_data.head() | code |
2022050/cell_22 | [
"text_html_output_1.png"
] | from pandas.io.json import json_normalize
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.concat(deserialize_cr, ignore_index=True)
df_cr.columns = ['Left Clan', 'Left Deck', 'Left Player', 'Left Trophy', 'Right Clan', 'Right Deck', 'Right Player', 'right Trophy', 'Result', 'Time', 'Type']
Left_Troops = list(np.hstack([[x[0] for x in left_deck] for left_deck in df_cr['Left Deck']]))
Right_Troops = list(np.hstack([[x[0] for x in right_deck] for right_deck in df_cr['Right Deck']]))
distinct_troops = set(np.hstack([Left_Troops, Right_Troops]))
len(distinct_troops)
RightArmy_colNames = np.hstack([['Right Troop ' + str(i + 1) for i in range(8)], ['Right Troop Count ' + str(i + 1) for i in range(8)]])
LeftArmy_colNames = np.hstack([['Left Troop ' + str(i + 1) for i in range(8)], ['Left Troop Count ' + str(i + 1) for i in range(8)]])
RightArmy = pd.DataFrame(data=[np.hstack([[army[0] for army in x], [army[1] for army in x]]) for x in df_cr['Right Deck']], columns=RightArmy_colNames)
LeftArmy = pd.DataFrame(data=[np.hstack([[army[0] for army in x], [army[1] for army in x]]) for x in df_cr['Left Deck']], columns=LeftArmy_colNames)
finalCR_data = pd.concat([df_cr, LeftArmy, RightArmy], axis=1, join='inner')
finalCR_data[(finalCR_data['Battle Result'] == 'Left') & (finalCR_data['Left Stars Won'] == '3')][['Left Troop 1', 'Left Troop 2', 'Left Troop 3', 'Left Troop 4', 'Left Troop 5', 'Left Troop 6', 'Left Troop 7', 'Left Troop 8', 'Result']].groupby(['Left Troop 1', 'Left Troop 2', 'Left Troop 3', 'Left Troop 4', 'Left Troop 5', 'Left Troop 6', 'Left Troop 7', 'Left Troop 8']).count().sort_values(by='Result', ascending=False) | code |
2022050/cell_5 | [
"text_html_output_1.png"
] | with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
len(CR) | code |
34137894/cell_21 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_data.select_dtypes(exclude='object')
num_cols = num_data.columns
num_to_cat_cols = ['MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond']
num_cols = [i for i in num_cols if not i in num_to_cat_cols]
num_cols = [i for i in num_cols if not i in ['Id']]
num_data = num_data.drop(['Id', 'MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond'], axis=1)
print('There are %d Num , %d Cat, %d Num-Cat columns.' % (len(num_cols), len(cat_cols), len(num_to_cat_cols))) | code |
34137894/cell_9 | [
"image_output_11.png",
"image_output_24.png",
"image_output_25.png",
"image_output_17.png",
"image_output_30.png",
"image_output_14.png",
"image_output_28.png",
"image_output_23.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_21.png",
"image_output_7.png",
"image_output_31.png",
"image_output_20.png",
"image_output_32.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.png",
"image_output_27.png",
"image_output_6.png",
"image_output_12.png",
"image_output_22.png",
"image_output_3.png",
"image_output_29.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_15.png",
"image_output_9.png",
"image_output_19.png",
"image_output_26.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
test_data.shape | code |
34137894/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize=(10,5))
sns.distplot(train_data['SalePrice'])
plt.tight_layout()
plt.show()
fig = plt.figure(figsize=(10,5))
sns.distplot(np.log1p(train_data['SalePrice']))
plt.tight_layout()
plt.show()
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_data.select_dtypes(exclude='object')
num_cols = num_data.columns
num_to_cat_cols = ['MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond']
num_cols = [i for i in num_cols if not i in num_to_cat_cols]
num_cols = [i for i in num_cols if not i in ['Id']]
num_data = num_data.drop(['Id', 'MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond'], axis=1)
for i in range(len(num_data.columns)):
f, ax = plt.subplots(figsize=(7, 4))
fig = sns.distplot(num_data.iloc[:, i].dropna(), rug=False, hist=False, kde_kws={'bw': 0.1})
plt.xlabel(num_data.columns[i]) | code |
34137894/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape | code |
34137894/cell_18 | [
"text_plain_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize=(10,5))
sns.distplot(train_data['SalePrice'])
plt.tight_layout()
plt.show()
from scipy import stats
fig = plt.figure(figsize=(10,5))
sns.distplot(np.log1p(train_data['SalePrice']))
plt.tight_layout()
plt.show()
stats.probplot(np.log1p(train_data['SalePrice']), plot=plt) | code |
34137894/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
train_data.info() | code |
34137894/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize=(10,5))
sns.distplot(train_data['SalePrice'])
plt.tight_layout()
plt.show()
fig = plt.figure(figsize=(10, 5))
sns.distplot(np.log1p(train_data['SalePrice']))
plt.tight_layout()
plt.show() | code |
34137894/cell_14 | [
"text_plain_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize=(10,5))
sns.distplot(train_data['SalePrice'])
plt.tight_layout()
plt.show()
from scipy import stats
stats.probplot(train_data['SalePrice'], plot=plt) | code |
34137894/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
test_data.shape
test_data.info() | code |
34137894/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize=(10, 5))
sns.distplot(train_data['SalePrice'])
plt.tight_layout()
plt.show() | code |
34137894/cell_5 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.head() | code |
128047114/cell_20 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader
from tqdm import tqdm
import json
import matplotlib.pyplot as plt
import os
import torch
import torchvision
import torchvision.transforms as T
import torch
from torch.utils.data import DataLoader
import torchvision
import torchvision.transforms as T
from torchvision.io import read_image, ImageReadMode
from sklearn.model_selection import train_test_split
import os
import json
from tqdm import tqdm
from PIL import Image
import matplotlib.pyplot as plt
import random
Image.MAX_IMAGE_PIXELS = None
ds_path = '/kaggle/input/raw-mmimdb/mmimdb/dataset/'
ids = list(set([x.split('.')[0] for x in os.listdir(ds_path)]))
img = f'{ds_path}{ids[0]}.jpeg'
meta = f'{ds_path}{ids[0]}.json'
Image.open(img)
with open(meta, 'r') as f:
metadata = json.load(f)
horror_ids = []
drama_ids = []
for id in tqdm(ids):
metadata_file = f'{ds_path}{id}.json'
with open(metadata_file, 'r') as f:
metadata = json.load(f)
genres = metadata['genres']
if 'Horror' in genres and 'Drama' not in genres:
horror_ids.append(id)
if 'Drama' in genres and 'Horror' not in genres:
drama_ids.append(id)
horror_ids = horror_ids[:2000]
drama_ids = drama_ids[:2000]
ids = horror_ids + drama_ids
labels = [0] * len(horror_ids) + [1] * len(drama_ids)
class ImageDataset(torch.utils.data.Dataset):
def __init__(self, image_folder, ids, labels, transform=None):
self.image_folder = image_folder
self.ids = ids
self.labels = labels
self.transform = transform
def __len__(self):
return len(self.ids)
def __getitem__(self, idx):
image_path = self.image_folder + self.ids[idx] + '.jpeg'
image = Image.open(image_path).convert('RGB')
label = self.labels[idx]
if self.transform:
image = self.transform(image)
return (image, label)
X_train, X_valid, y_train, y_valid = train_test_split(ids, labels, test_size=0.4, random_state=42, stratify=labels)
X_valid, X_test, y_valid, y_test = train_test_split(X_valid, y_valid, test_size=0.5, random_state=42, stratify=y_valid)
img_height, img_width = (512, 512)
transform = T.Compose([T.ToTensor(), T.Resize((img_height, img_width)), T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
train_ds = ImageDataset(ds_path, X_train, y_train, transform=transform)
valid_ds = ImageDataset(ds_path, X_valid, y_valid, transform=transform)
test_ds = ImageDataset(ds_path, X_test, y_test, transform=transform)
batch_size = 10
device = 'cuda' if torch.cuda.is_available() else 'cpu'
loader_args = dict(batch_size=batch_size, num_workers=os.cpu_count(), pin_memory=True, shuffle=True)
train_dl = DataLoader(train_ds, **loader_args)
valid_dl = DataLoader(valid_ds, **loader_args)
test_dl = DataLoader(test_ds, **loader_args)
model = torchvision.models.resnet50(weights=torchvision.models.resnet.ResNet50_Weights.IMAGENET1K_V1)
model
for param in model.parameters():
param.requires_grad = False
model.fc = torch.nn.Linear(model.fc.in_features, 2)
loss_fn = torch.nn.CrossEntropyLoss()
model.to(device)
params_to_update = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.Adam(params_to_update, lr=0.0003)
epochs = 7
train_losses = []
valid_losses = []
for epoch in range(epochs):
running_loss = 0.0
running_corrects = 0
model.train()
with tqdm(total=len(train_ds), desc=f'Train epoch {epoch}/{epochs}', unit='img') as pb:
for X, y in train_dl:
X, y = (X.to(device), y.to(device))
pred = model(X)
loss = loss_fn(pred, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
running_loss += loss.item() * X.size(0)
running_corrects += torch.sum(pred.argmax(1) == y.data)
pb.update(X.shape[0])
pb.set_postfix(**{'loss (batch)': loss.item()})
epoch_loss = running_loss / len(train_ds)
train_losses.append(epoch_loss)
epoch_acc = running_corrects / len(train_ds)
running_loss = 0.0
model.eval()
with torch.no_grad():
corrects = 0
with tqdm(total=len(valid_ds), desc=f'Validation epoch {epoch}/{epochs - 1}', unit='img') as pb:
for X, y in valid_dl:
X, y = (X.to(device), y.to(device))
outputs = model(X)
_, predicted = torch.max(outputs, 1)
loss = loss_fn(outputs, y)
running_loss += loss.item() * X.size(0)
corrects += (predicted == y).sum().item()
pb.update(X.shape[0])
epoch_loss = running_loss / len(valid_ds)
accuracy = corrects / len(valid_ds)
valid_losses.append(epoch_loss)
plt.plot(range(epochs), train_losses, label='train')
plt.plot(range(epochs), valid_losses, label='valid')
plt.title('Train and test loss')
plt.legend()
plt.show() | code |
128047114/cell_6 | [
"image_output_1.png"
] | from PIL import Image
import json
import os
import torch
from torch.utils.data import DataLoader
import torchvision
import torchvision.transforms as T
from torchvision.io import read_image, ImageReadMode
from sklearn.model_selection import train_test_split
import os
import json
from tqdm import tqdm
from PIL import Image
import matplotlib.pyplot as plt
import random
Image.MAX_IMAGE_PIXELS = None
ds_path = '/kaggle/input/raw-mmimdb/mmimdb/dataset/'
ids = list(set([x.split('.')[0] for x in os.listdir(ds_path)]))
img = f'{ds_path}{ids[0]}.jpeg'
meta = f'{ds_path}{ids[0]}.json'
Image.open(img)
with open(meta, 'r') as f:
metadata = json.load(f)
print('Genres:', metadata['genres']) | code |
128047114/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
ds_path = '/kaggle/input/raw-mmimdb/mmimdb/dataset/'
ids = list(set([x.split('.')[0] for x in os.listdir(ds_path)]))
print('Some image ids:', ids[:5]) | code |
128047114/cell_19 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader
from tqdm import tqdm
import json
import os
import torch
import torchvision
import torchvision.transforms as T
import torch
from torch.utils.data import DataLoader
import torchvision
import torchvision.transforms as T
from torchvision.io import read_image, ImageReadMode
from sklearn.model_selection import train_test_split
import os
import json
from tqdm import tqdm
from PIL import Image
import matplotlib.pyplot as plt
import random
Image.MAX_IMAGE_PIXELS = None
ds_path = '/kaggle/input/raw-mmimdb/mmimdb/dataset/'
ids = list(set([x.split('.')[0] for x in os.listdir(ds_path)]))
img = f'{ds_path}{ids[0]}.jpeg'
meta = f'{ds_path}{ids[0]}.json'
Image.open(img)
with open(meta, 'r') as f:
metadata = json.load(f)
horror_ids = []
drama_ids = []
for id in tqdm(ids):
metadata_file = f'{ds_path}{id}.json'
with open(metadata_file, 'r') as f:
metadata = json.load(f)
genres = metadata['genres']
if 'Horror' in genres and 'Drama' not in genres:
horror_ids.append(id)
if 'Drama' in genres and 'Horror' not in genres:
drama_ids.append(id)
horror_ids = horror_ids[:2000]
drama_ids = drama_ids[:2000]
ids = horror_ids + drama_ids
labels = [0] * len(horror_ids) + [1] * len(drama_ids)
class ImageDataset(torch.utils.data.Dataset):
def __init__(self, image_folder, ids, labels, transform=None):
self.image_folder = image_folder
self.ids = ids
self.labels = labels
self.transform = transform
def __len__(self):
return len(self.ids)
def __getitem__(self, idx):
image_path = self.image_folder + self.ids[idx] + '.jpeg'
image = Image.open(image_path).convert('RGB')
label = self.labels[idx]
if self.transform:
image = self.transform(image)
return (image, label)
X_train, X_valid, y_train, y_valid = train_test_split(ids, labels, test_size=0.4, random_state=42, stratify=labels)
X_valid, X_test, y_valid, y_test = train_test_split(X_valid, y_valid, test_size=0.5, random_state=42, stratify=y_valid)
img_height, img_width = (512, 512)
transform = T.Compose([T.ToTensor(), T.Resize((img_height, img_width)), T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
train_ds = ImageDataset(ds_path, X_train, y_train, transform=transform)
valid_ds = ImageDataset(ds_path, X_valid, y_valid, transform=transform)
test_ds = ImageDataset(ds_path, X_test, y_test, transform=transform)
batch_size = 10
device = 'cuda' if torch.cuda.is_available() else 'cpu'
loader_args = dict(batch_size=batch_size, num_workers=os.cpu_count(), pin_memory=True, shuffle=True)
train_dl = DataLoader(train_ds, **loader_args)
valid_dl = DataLoader(valid_ds, **loader_args)
test_dl = DataLoader(test_ds, **loader_args)
model = torchvision.models.resnet50(weights=torchvision.models.resnet.ResNet50_Weights.IMAGENET1K_V1)
model
for param in model.parameters():
param.requires_grad = False
model.fc = torch.nn.Linear(model.fc.in_features, 2)
loss_fn = torch.nn.CrossEntropyLoss()
model.to(device)
params_to_update = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.Adam(params_to_update, lr=0.0003)
epochs = 7
train_losses = []
valid_losses = []
for epoch in range(epochs):
running_loss = 0.0
running_corrects = 0
model.train()
with tqdm(total=len(train_ds), desc=f'Train epoch {epoch}/{epochs}', unit='img') as pb:
for X, y in train_dl:
X, y = (X.to(device), y.to(device))
pred = model(X)
loss = loss_fn(pred, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
running_loss += loss.item() * X.size(0)
running_corrects += torch.sum(pred.argmax(1) == y.data)
pb.update(X.shape[0])
pb.set_postfix(**{'loss (batch)': loss.item()})
epoch_loss = running_loss / len(train_ds)
train_losses.append(epoch_loss)
epoch_acc = running_corrects / len(train_ds)
print(f'Train Loss: {epoch_loss:.4f} Accuracy: {epoch_acc:.4f}')
running_loss = 0.0
model.eval()
with torch.no_grad():
corrects = 0
with tqdm(total=len(valid_ds), desc=f'Validation epoch {epoch}/{epochs - 1}', unit='img') as pb:
for X, y in valid_dl:
X, y = (X.to(device), y.to(device))
outputs = model(X)
_, predicted = torch.max(outputs, 1)
loss = loss_fn(outputs, y)
running_loss += loss.item() * X.size(0)
corrects += (predicted == y).sum().item()
pb.update(X.shape[0])
epoch_loss = running_loss / len(valid_ds)
accuracy = corrects / len(valid_ds)
valid_losses.append(epoch_loss)
print(f'Validation Accuraccy: {accuracy:.4f}') | code |
128047114/cell_8 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from tqdm import tqdm
import json
import os
import torch
from torch.utils.data import DataLoader
import torchvision
import torchvision.transforms as T
from torchvision.io import read_image, ImageReadMode
from sklearn.model_selection import train_test_split
import os
import json
from tqdm import tqdm
from PIL import Image
import matplotlib.pyplot as plt
import random
Image.MAX_IMAGE_PIXELS = None
ds_path = '/kaggle/input/raw-mmimdb/mmimdb/dataset/'
ids = list(set([x.split('.')[0] for x in os.listdir(ds_path)]))
img = f'{ds_path}{ids[0]}.jpeg'
meta = f'{ds_path}{ids[0]}.json'
Image.open(img)
with open(meta, 'r') as f:
metadata = json.load(f)
horror_ids = []
drama_ids = []
for id in tqdm(ids):
metadata_file = f'{ds_path}{id}.json'
with open(metadata_file, 'r') as f:
metadata = json.load(f)
genres = metadata['genres']
if 'Horror' in genres and 'Drama' not in genres:
horror_ids.append(id)
if 'Drama' in genres and 'Horror' not in genres:
drama_ids.append(id) | code |
128047114/cell_16 | [
"image_output_1.png"
] | import torchvision
model = torchvision.models.resnet50(weights=torchvision.models.resnet.ResNet50_Weights.IMAGENET1K_V1)
model | code |
128047114/cell_14 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader
from tqdm import tqdm
import json
import os
import torch
import torchvision.transforms as T
import torch
from torch.utils.data import DataLoader
import torchvision
import torchvision.transforms as T
from torchvision.io import read_image, ImageReadMode
from sklearn.model_selection import train_test_split
import os
import json
from tqdm import tqdm
from PIL import Image
import matplotlib.pyplot as plt
import random
Image.MAX_IMAGE_PIXELS = None
ds_path = '/kaggle/input/raw-mmimdb/mmimdb/dataset/'
ids = list(set([x.split('.')[0] for x in os.listdir(ds_path)]))
img = f'{ds_path}{ids[0]}.jpeg'
meta = f'{ds_path}{ids[0]}.json'
Image.open(img)
with open(meta, 'r') as f:
metadata = json.load(f)
horror_ids = []
drama_ids = []
for id in tqdm(ids):
metadata_file = f'{ds_path}{id}.json'
with open(metadata_file, 'r') as f:
metadata = json.load(f)
genres = metadata['genres']
if 'Horror' in genres and 'Drama' not in genres:
horror_ids.append(id)
if 'Drama' in genres and 'Horror' not in genres:
drama_ids.append(id)
horror_ids = horror_ids[:2000]
drama_ids = drama_ids[:2000]
ids = horror_ids + drama_ids
labels = [0] * len(horror_ids) + [1] * len(drama_ids)
class ImageDataset(torch.utils.data.Dataset):
def __init__(self, image_folder, ids, labels, transform=None):
self.image_folder = image_folder
self.ids = ids
self.labels = labels
self.transform = transform
def __len__(self):
return len(self.ids)
def __getitem__(self, idx):
image_path = self.image_folder + self.ids[idx] + '.jpeg'
image = Image.open(image_path).convert('RGB')
label = self.labels[idx]
if self.transform:
image = self.transform(image)
return (image, label)
X_train, X_valid, y_train, y_valid = train_test_split(ids, labels, test_size=0.4, random_state=42, stratify=labels)
X_valid, X_test, y_valid, y_test = train_test_split(X_valid, y_valid, test_size=0.5, random_state=42, stratify=y_valid)
img_height, img_width = (512, 512)
transform = T.Compose([T.ToTensor(), T.Resize((img_height, img_width)), T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
train_ds = ImageDataset(ds_path, X_train, y_train, transform=transform)
valid_ds = ImageDataset(ds_path, X_valid, y_valid, transform=transform)
test_ds = ImageDataset(ds_path, X_test, y_test, transform=transform)
batch_size = 10
device = 'cuda' if torch.cuda.is_available() else 'cpu'
loader_args = dict(batch_size=batch_size, num_workers=os.cpu_count(), pin_memory=True, shuffle=True)
train_dl = DataLoader(train_ds, **loader_args)
valid_dl = DataLoader(valid_ds, **loader_args)
test_dl = DataLoader(test_ds, **loader_args)
print('Sizes of the datasets: ', len(train_ds), len(valid_ds), len(test_ds)) | code |
128047114/cell_5 | [
"application_vnd.jupyter.stderr_output_27.png",
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_11.png",
"text_plain_output_20.png",
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_25.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_24.png",
"application_vnd.jupyter.stderr_output_23.png",
"text_plain_output_18.png",
"application_vnd.jupyter.stderr_output_19.png",
"application_vnd.jupyter.stderr_output_13.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_22.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_16.png",
"application_vnd.jupyter.stderr_output_15.png",
"text_plain_output_8.png",
"application_vnd.jupyter.stderr_output_17.png",
"text_plain_output_26.png",
"text_plain_output_28.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_29.png",
"application_vnd.jupyter.stderr_output_1.png",
"text_plain_output_12.png",
"application_vnd.jupyter.stderr_output_21.png"
] | from PIL import Image
import os
import torch
from torch.utils.data import DataLoader
import torchvision
import torchvision.transforms as T
from torchvision.io import read_image, ImageReadMode
from sklearn.model_selection import train_test_split
import os
import json
from tqdm import tqdm
from PIL import Image
import matplotlib.pyplot as plt
import random
Image.MAX_IMAGE_PIXELS = None
ds_path = '/kaggle/input/raw-mmimdb/mmimdb/dataset/'
ids = list(set([x.split('.')[0] for x in os.listdir(ds_path)]))
img = f'{ds_path}{ids[0]}.jpeg'
meta = f'{ds_path}{ids[0]}.json'
Image.open(img) | code |
73088112/cell_21 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
test.select_dtypes(exclude=[np.number]).describe() | code |
73088112/cell_25 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
def missing_values_table(df):
mis_val = df.isnull().sum()
mis_val_percent = 100 * df.isnull().sum() / len(df)
mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)
mis_val_table_ren_columns = mis_val_table.rename(columns={0: 'Missing Values', 1: '% of Total Values'})
mis_val_table_ren_columns = mis_val_table_ren_columns[mis_val_table_ren_columns.iloc[:, 1] != 0].sort_values('% of Total Values', ascending=False).round(1)
return mis_val_table_ren_columns
missing_values_table(train) | code |
73088112/cell_34 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
train.Price.describe() | code |
73088112/cell_23 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
display(train[train.BuildingArea == 0].head(5))
display(train[train.BuildingArea == 0].shape) | code |
73088112/cell_20 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
train.select_dtypes(exclude=[np.number]).describe() | code |
73088112/cell_29 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
train.Price.describe() | code |
73088112/cell_39 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from scipy import stats
from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
sns.distplot(train['Price'], fit=norm);
fig = plt.figure()
res = stats.probplot(train['Price'], plot=plt)
print ("Skew is:", train.Price.skew())
sns.distplot(np.log1p(train['Price']), fit=norm)
fig = plt.figure()
res = stats.probplot(np.log1p(train['Price']), plot=plt)
print('Skew is:', np.log1p(train.Price).skew()) | code |
73088112/cell_26 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
def missing_values_table(df):
mis_val = df.isnull().sum()
mis_val_percent = 100 * df.isnull().sum() / len(df)
mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)
mis_val_table_ren_columns = mis_val_table.rename(columns={0: 'Missing Values', 1: '% of Total Values'})
mis_val_table_ren_columns = mis_val_table_ren_columns[mis_val_table_ren_columns.iloc[:, 1] != 0].sort_values('% of Total Values', ascending=False).round(1)
return mis_val_table_ren_columns
missing_values_table(train)
missing_values_table(test) | code |
73088112/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
print(duplicate_row) | code |
73088112/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
test.describe() | code |
73088112/cell_7 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
train.head(5) | code |
73088112/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
train.describe() | code |
73088112/cell_8 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
print(f'Kích thước tập train: {train.shape}')
print(f'Kích thước tập test: {test.shape}') | code |
73088112/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts() | code |
73088112/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy import stats
from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
sns.distplot(train['Price'], fit=norm)
fig = plt.figure()
res = stats.probplot(train['Price'], plot=plt)
print('Skew is:', train.Price.skew()) | code |
73088112/cell_31 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
sns.distplot(train['Price']) | code |
73088112/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.info() | code |
73088112/cell_36 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
sns.distplot(train['Price'], fit=norm);
fig = plt.figure()
res = stats.probplot(train['Price'], plot=plt)
print ("Skew is:", train.Price.skew())
sns.boxplot(train['Price'], orient='v') | code |
128014397/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | autoencoder.fit(X_train, X_train, epochs=100, batch_size=32, shuffle=True, validation_data=(X_test, X_test), verbose=0) | code |
128014397/cell_20 | [
"text_plain_output_1.png"
] | from keras.utils import to_categorical
from pathlib import Path
from sklearn.preprocessing import OrdinalEncoder
import numpy as np
import pandas as pd
INPUT_DIR = Path('/kaggle/input/playground-series-s3e13/')
train_data = pd.read_csv(INPUT_DIR / 'train.csv')
train_data['data_type'] = 0
train_data['prognosis'] = train_data['prognosis'].str.replace(' ', '_')
test_data = pd.read_csv(INPUT_DIR / 'test.csv')
test_data['data_type'] = 0
features = sorted(list(set(test_data.columns) - set(['id', 'data_type'])))
if INCLUDE_ORIGINAL:
df_original = pd.read_csv('/kaggle/input/vector-borne-disease-prediction/trainn.csv')
df_original['id'] = -1 - np.arange(len(df_original))
df_original['prognosis'] = df_original['prognosis'].str.replace(' ', '_')
df_original['data_type'] = 1
train_data = pd.concat([df_original, train_data]).reset_index(drop=True)
features = sorted(list(set(test_data.columns) - set(['id'])))
from sklearn.preprocessing import OrdinalEncoder
from keras.utils import to_categorical
enc = OrdinalEncoder()
y_enc = enc.fit_transform(train_data.filter(['prognosis']))
y = to_categorical(y_enc)
prognosis_classes = list(enc.categories_[0])
N_CLASSES = len(prognosis_classes)
feats = list(features)
X = train_data.filter(feats).values
X_train = train_data.drop('data_type', axis=1).filter(feats).values
X_data_type = train_data['data_type'].values
X_test = test_data.drop('data_type', axis=1).filter(feats).values
X_tst = test_data.filter(feats).values
n_components = 5
decomp = Decomp(n_components=n_components, method='umap', scaler_method=None)
umap_train = decomp.dimension_reduction(X_train)
umap_test = decomp.transform(X_test)
n_components = 7
decomp = Decomp(n_components=n_components, method='pca', scaler_method=None)
pca_train = decomp.dimension_reduction(X_train)
pca_test = decomp.transform(X_test)
n_components = 8
decomp = Decomp(n_components=n_components, method='LDA', scaler_method=None)
lda_train = decomp.dimension_reduction(X_train, y_enc.flatten())
lda_test = decomp.transform(X_test)
print(f' --> lda(n_components={n_components})') | code |
128014397/cell_26 | [
"text_plain_output_1.png"
] | from colorama import Fore, Back, Style
from keras.utils import to_categorical
from pathlib import Path
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.metrics import log_loss
from sklearn.model_selection import StratifiedKFold, KFold
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler, QuantileTransformer, RobustScaler
from sklearn.preprocessing import OrdinalEncoder
from tensorflow import keras
from tensorflow.keras import layers, models, Sequential
from umap import UMAP
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_ranking as tfr
INPUT_DIR = Path('/kaggle/input/playground-series-s3e13/')
train_data = pd.read_csv(INPUT_DIR / 'train.csv')
train_data['data_type'] = 0
train_data['prognosis'] = train_data['prognosis'].str.replace(' ', '_')
test_data = pd.read_csv(INPUT_DIR / 'test.csv')
test_data['data_type'] = 0
features = sorted(list(set(test_data.columns) - set(['id', 'data_type'])))
if INCLUDE_ORIGINAL:
df_original = pd.read_csv('/kaggle/input/vector-borne-disease-prediction/trainn.csv')
df_original['id'] = -1 - np.arange(len(df_original))
df_original['prognosis'] = df_original['prognosis'].str.replace(' ', '_')
df_original['data_type'] = 1
train_data = pd.concat([df_original, train_data]).reset_index(drop=True)
features = sorted(list(set(test_data.columns) - set(['id'])))
from sklearn.preprocessing import OrdinalEncoder
from keras.utils import to_categorical
enc = OrdinalEncoder()
y_enc = enc.fit_transform(train_data.filter(['prognosis']))
y = to_categorical(y_enc)
prognosis_classes = list(enc.categories_[0])
N_CLASSES = len(prognosis_classes)
feats = list(features)
X = train_data.filter(feats).values
X_train = train_data.drop('data_type', axis=1).filter(feats).values
X_data_type = train_data['data_type'].values
X_test = test_data.drop('data_type', axis=1).filter(feats).values
X_tst = test_data.filter(feats).values
encoder_dim = 12
dim_input = layers.Input(shape=(64,))
encoded_layer_0 = tf.keras.layers.GaussianNoise(0.1)(dim_input)
encoded_layer_1 = layers.Dense(60, activation='relu')(encoded_layer_0)
encoded_layer_2 = layers.Dense(50, activation='relu')(encoded_layer_1)
encoded_layer_3 = layers.Dense(40, activation='relu')(encoded_layer_2)
encoded_layer_4 = layers.Dense(30, activation='relu')(encoded_layer_3)
encoded_layer_5 = layers.Dense(20, activation='relu')(encoded_layer_4)
encoded_layer_6 = layers.Dense(encoder_dim, activation='softmax')(encoded_layer_5)
decoded_layer_1 = layers.Dense(20, activation='relu')(encoded_layer_6)
decoded_layer_2 = layers.Dense(30, activation='relu')(decoded_layer_1)
decoded_layer_3 = layers.Dense(40, activation='relu')(decoded_layer_2)
decoded_layer_4 = layers.Dense(50, activation='relu')(decoded_layer_3)
decoded_layer_5 = layers.Dense(60, activation='relu')(decoded_layer_4)
decoded_layer_6 = layers.Dense(64, activation='relu')(decoded_layer_5)
autoencoder = keras.Model(inputs=dim_input, outputs=decoded_layer_6)
autoencoder.compile(loss='categorical_crossentropy', metrics='categorical_accuracy')
encoder = keras.Model(inputs=dim_input, outputs=encoded_layer_6)
encoded_input = layers.Input(shape=(encoder_dim,))
encoded_train = pd.DataFrame(encoder.predict(X_train))
encoded_train = encoded_train.add_prefix('feature_')
encoded_test = pd.DataFrame(encoder.predict(X_test))
encoded_test = encoded_test.add_prefix('feature_')
class Decomp:
def __init__(self, n_components, method='pca', scaler_method='standard'):
self.n_components = n_components
self.method = method
self.scaler_method = scaler_method
def dimension_reduction(self, df, y=None):
if self.method == 'LDA':
X_reduced = self.dimension_method(df, y)
else:
X_reduced = self.dimension_method(df)
df_comp = pd.DataFrame(X_reduced, columns=[f'{self.method.upper()}_{_}' for _ in range(self.n_components)])
return df_comp
def dimension_method(self, df, y=None):
X = self.scaler(df)
if self.method == 'pca':
comp = PCA(n_components=self.n_components, random_state=0)
X_reduced = comp.fit_transform(X)
elif self.method == 'nmf':
comp = NMF(n_components=self.n_components, random_state=0)
X_reduced = comp.fit_transform(X)
elif self.method == 'umap':
comp = UMAP(n_components=self.n_components, random_state=0)
X_reduced = comp.fit_transform(X)
elif self.method == 'tsne':
comp = TSNE(n_components=self.n_components, random_state=0)
X_reduced = comp.fit_transform(X)
elif self.method == 'LDA':
comp = LinearDiscriminantAnalysis(n_components=self.n_components)
X_reduced = comp.fit_transform(X, y)
else:
raise ValueError(f'Invalid method name: {method}')
self.comp = comp
return X_reduced
def scaler(self, df):
_df = df.copy()
if self.scaler_method == 'standard':
return StandardScaler().fit_transform(_df)
elif self.scaler_method == 'minmax':
return MinMaxScaler().fit_transform(_df)
elif self.scaler_method == None:
return _df
else:
raise ValueError(f'Invalid scaler_method name')
def get_columns(self):
return [f'{self.method.upper()}_{_}' for _ in range(self.n_components)]
def transform(self, df, y=None):
X = self.scaler(df)
X_reduced = self.comp.transform(X)
df_comp = pd.DataFrame(X_reduced, columns=[f'{self.method.upper()}_{_}' for _ in range(self.n_components)])
return df_comp
@property
def get_explained_variance_ratio(self):
return np.sum(self.comp.explained_variance_ratio_)
n_components = 5
decomp = Decomp(n_components=n_components, method='umap', scaler_method=None)
umap_train = decomp.dimension_reduction(X_train)
umap_test = decomp.transform(X_test)
n_components = 7
decomp = Decomp(n_components=n_components, method='pca', scaler_method=None)
pca_train = decomp.dimension_reduction(X_train)
pca_test = decomp.transform(X_test)
n_components = 8
decomp = Decomp(n_components=n_components, method='LDA', scaler_method=None)
lda_train = decomp.dimension_reduction(X_train, y_enc.flatten())
lda_test = decomp.transform(X_test)
new_all_data = pd.concat([train_data, umap_train, lda_train], axis=1)
new_test = pd.concat([test_data, umap_test, lda_test], axis=1)
def build_model(input_shape):
def mish(x):
return keras.layers.Lambda(lambda x: x * K.tanh(K.softplus(x)))(x)
inputs = layers.Input(shape=input_shape)
x = layers.Dropout(0.2)(inputs)
x = layers.Dense(64, activation=mish)(x)
for _ in range(1):
x = layers.BatchNormalization()(x)
x = layers.Dense(32, activation=mish)(x)
x = layers.Dropout(0.5)(x)
outputs = layers.Dense(N_CLASSES, activation='softmax')(x)
return keras.Model(inputs, outputs)
epochs = 100
lr_start = 0.002
lr_end = 1e-07
def cosine_decay(epoch):
if epochs > 1:
w = (1 + np.cos(epoch / (epochs - 1) * np.pi)) / 2
else:
w = 1
return w * lr_start + (1 - w) * lr_end
lr = keras.callbacks.LearningRateScheduler(cosine_decay, verbose=0)
callbacks = [lr, keras.callbacks.TerminateOnNaN()]
def map3_from_logloss(y_enc, preds, data_type):
return map3(y_enc[data_type == 0], preds[data_type == 0])
def fold_logloss(y_enc, preds, data_type):
return log_loss(y_enc[data_type == 0], preds[data_type == 0])
N_FOLDS = 10
N_REPEATS = 10
features = sorted(list(set(new_test.columns) - set(['id', 'data_type'])))
feats = list(features)
X = new_all_data.filter(feats).values.astype(np.float32)
X_data_type = new_all_data['data_type'].values.astype(np.float32)
X_test = new_test.filter(feats).values.astype(np.float32)
oof_preds = np.zeros((len(train_data), N_CLASSES))
test_preds = np.zeros((len(test_data), N_CLASSES))
oof_metrics = []
oof_lls = []
for i in range(N_REPEATS):
k_fold = StratifiedKFold(n_splits=N_FOLDS, random_state=RANDOM_STATE + i, shuffle=True)
for train_index, test_index in k_fold.split(X, y_enc.flatten()):
X_train, X_valid = (X[train_index], X[test_index])
y_train, y_valid = (y[train_index], y[test_index])
model = build_model(input_shape=(len(feats),))
model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=[tfr.keras.metrics.MeanAveragePrecisionMetric(topn=3)])
history = model.fit(X_train, y_train, validation_data=(X_valid, y_valid), batch_size=32, epochs=epochs, callbacks=callbacks, verbose=0)
oof_pred = model.predict(X_valid, verbose=0)
test_pred = model.predict(X_test, verbose=0)
oof_metric = map3_from_logloss(y_valid, oof_pred, X_data_type[test_index])
oof_ll = fold_logloss(y_valid, oof_pred, X_data_type[test_index])
oof_metrics.append(oof_metric)
oof_lls.append(oof_ll)
oof_preds[test_index] += oof_pred / N_REPEATS
test_preds += test_pred / (N_REPEATS * N_FOLDS)
oof_metric = np.round(np.mean(oof_metrics), 5)
oof_ll = np.round(np.mean(oof_lls), 5)
print(f'{Fore.GREEN}{Style.BRIGHT}Average metric = {round(oof_metric, 5)}{Style.RESET_ALL}') | code |
128014397/cell_19 | [
"text_plain_output_1.png"
] | from keras.utils import to_categorical
from pathlib import Path
from sklearn.preprocessing import OrdinalEncoder
import numpy as np
import pandas as pd
INPUT_DIR = Path('/kaggle/input/playground-series-s3e13/')
train_data = pd.read_csv(INPUT_DIR / 'train.csv')
train_data['data_type'] = 0
train_data['prognosis'] = train_data['prognosis'].str.replace(' ', '_')
test_data = pd.read_csv(INPUT_DIR / 'test.csv')
test_data['data_type'] = 0
features = sorted(list(set(test_data.columns) - set(['id', 'data_type'])))
if INCLUDE_ORIGINAL:
df_original = pd.read_csv('/kaggle/input/vector-borne-disease-prediction/trainn.csv')
df_original['id'] = -1 - np.arange(len(df_original))
df_original['prognosis'] = df_original['prognosis'].str.replace(' ', '_')
df_original['data_type'] = 1
train_data = pd.concat([df_original, train_data]).reset_index(drop=True)
features = sorted(list(set(test_data.columns) - set(['id'])))
from sklearn.preprocessing import OrdinalEncoder
from keras.utils import to_categorical
enc = OrdinalEncoder()
y_enc = enc.fit_transform(train_data.filter(['prognosis']))
y = to_categorical(y_enc)
prognosis_classes = list(enc.categories_[0])
N_CLASSES = len(prognosis_classes)
feats = list(features)
X = train_data.filter(feats).values
X_train = train_data.drop('data_type', axis=1).filter(feats).values
X_data_type = train_data['data_type'].values
X_test = test_data.drop('data_type', axis=1).filter(feats).values
X_tst = test_data.filter(feats).values
n_components = 5
decomp = Decomp(n_components=n_components, method='umap', scaler_method=None)
umap_train = decomp.dimension_reduction(X_train)
umap_test = decomp.transform(X_test)
n_components = 7
decomp = Decomp(n_components=n_components, method='pca', scaler_method=None)
pca_train = decomp.dimension_reduction(X_train)
pca_test = decomp.transform(X_test)
print(f' --> pca(n_components={n_components})') | code |
128014397/cell_18 | [
"text_plain_output_1.png"
] | from keras.utils import to_categorical
from pathlib import Path
from sklearn.preprocessing import OrdinalEncoder
import numpy as np
import pandas as pd
INPUT_DIR = Path('/kaggle/input/playground-series-s3e13/')
train_data = pd.read_csv(INPUT_DIR / 'train.csv')
train_data['data_type'] = 0
train_data['prognosis'] = train_data['prognosis'].str.replace(' ', '_')
test_data = pd.read_csv(INPUT_DIR / 'test.csv')
test_data['data_type'] = 0
features = sorted(list(set(test_data.columns) - set(['id', 'data_type'])))
if INCLUDE_ORIGINAL:
df_original = pd.read_csv('/kaggle/input/vector-borne-disease-prediction/trainn.csv')
df_original['id'] = -1 - np.arange(len(df_original))
df_original['prognosis'] = df_original['prognosis'].str.replace(' ', '_')
df_original['data_type'] = 1
train_data = pd.concat([df_original, train_data]).reset_index(drop=True)
features = sorted(list(set(test_data.columns) - set(['id'])))
from sklearn.preprocessing import OrdinalEncoder
from keras.utils import to_categorical
enc = OrdinalEncoder()
y_enc = enc.fit_transform(train_data.filter(['prognosis']))
y = to_categorical(y_enc)
prognosis_classes = list(enc.categories_[0])
N_CLASSES = len(prognosis_classes)
feats = list(features)
X = train_data.filter(feats).values
X_train = train_data.drop('data_type', axis=1).filter(feats).values
X_data_type = train_data['data_type'].values
X_test = test_data.drop('data_type', axis=1).filter(feats).values
X_tst = test_data.filter(feats).values
n_components = 5
decomp = Decomp(n_components=n_components, method='umap', scaler_method=None)
umap_train = decomp.dimension_reduction(X_train)
umap_test = decomp.transform(X_test)
print(f' --> UMAP(n_components={n_components})') | code |
128014397/cell_15 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.utils import to_categorical
from pathlib import Path
from sklearn.preprocessing import OrdinalEncoder
from tensorflow import keras
from tensorflow.keras import layers, models, Sequential
import numpy as np
import pandas as pd
import tensorflow as tf
INPUT_DIR = Path('/kaggle/input/playground-series-s3e13/')
train_data = pd.read_csv(INPUT_DIR / 'train.csv')
train_data['data_type'] = 0
train_data['prognosis'] = train_data['prognosis'].str.replace(' ', '_')
test_data = pd.read_csv(INPUT_DIR / 'test.csv')
test_data['data_type'] = 0
features = sorted(list(set(test_data.columns) - set(['id', 'data_type'])))
if INCLUDE_ORIGINAL:
df_original = pd.read_csv('/kaggle/input/vector-borne-disease-prediction/trainn.csv')
df_original['id'] = -1 - np.arange(len(df_original))
df_original['prognosis'] = df_original['prognosis'].str.replace(' ', '_')
df_original['data_type'] = 1
train_data = pd.concat([df_original, train_data]).reset_index(drop=True)
features = sorted(list(set(test_data.columns) - set(['id'])))
from sklearn.preprocessing import OrdinalEncoder
from keras.utils import to_categorical
enc = OrdinalEncoder()
y_enc = enc.fit_transform(train_data.filter(['prognosis']))
y = to_categorical(y_enc)
prognosis_classes = list(enc.categories_[0])
N_CLASSES = len(prognosis_classes)
feats = list(features)
X = train_data.filter(feats).values
X_train = train_data.drop('data_type', axis=1).filter(feats).values
X_data_type = train_data['data_type'].values
X_test = test_data.drop('data_type', axis=1).filter(feats).values
X_tst = test_data.filter(feats).values
encoder_dim = 12
dim_input = layers.Input(shape=(64,))
encoded_layer_0 = tf.keras.layers.GaussianNoise(0.1)(dim_input)
encoded_layer_1 = layers.Dense(60, activation='relu')(encoded_layer_0)
encoded_layer_2 = layers.Dense(50, activation='relu')(encoded_layer_1)
encoded_layer_3 = layers.Dense(40, activation='relu')(encoded_layer_2)
encoded_layer_4 = layers.Dense(30, activation='relu')(encoded_layer_3)
encoded_layer_5 = layers.Dense(20, activation='relu')(encoded_layer_4)
encoded_layer_6 = layers.Dense(encoder_dim, activation='softmax')(encoded_layer_5)
decoded_layer_1 = layers.Dense(20, activation='relu')(encoded_layer_6)
decoded_layer_2 = layers.Dense(30, activation='relu')(decoded_layer_1)
decoded_layer_3 = layers.Dense(40, activation='relu')(decoded_layer_2)
decoded_layer_4 = layers.Dense(50, activation='relu')(decoded_layer_3)
decoded_layer_5 = layers.Dense(60, activation='relu')(decoded_layer_4)
decoded_layer_6 = layers.Dense(64, activation='relu')(decoded_layer_5)
autoencoder = keras.Model(inputs=dim_input, outputs=decoded_layer_6)
autoencoder.compile(loss='categorical_crossentropy', metrics='categorical_accuracy')
encoder = keras.Model(inputs=dim_input, outputs=encoded_layer_6)
encoded_input = layers.Input(shape=(encoder_dim,))
encoded_train = pd.DataFrame(encoder.predict(X_train))
encoded_train = encoded_train.add_prefix('feature_')
encoded_test = pd.DataFrame(encoder.predict(X_test))
encoded_test = encoded_test.add_prefix('feature_') | code |
128014397/cell_3 | [
"text_plain_output_1.png"
] | import math
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', None)
from pathlib import Path
from plotnine import *
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, models, Sequential
from tensorflow.keras import backend as K
import tensorflow_ranking as tfr
from sklearn.model_selection import StratifiedKFold, KFold
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler, QuantileTransformer, RobustScaler
from sklearn.metrics import log_loss
from colorama import Fore, Back, Style
from tqdm import tqdm
from umap import UMAP
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
def map3(y_true, y_pred, **kwargs):
map3_metric = tfr.keras.metrics.MeanAveragePrecisionMetric(topn=3)
return map3_metric(y_true, y_pred, **kwargs).numpy()
RANDOM_STATE = 42
INCLUDE_ORIGINAL = False | code |
105186076/cell_13 | [
"image_output_1.png"
] | import numpy as np
X.ravel()[:5]
m = 78.35
b1 = 100
loss_slope = -2 * np.sum(y - m * X.ravel() - b1)
lr = 0.1
step_size = loss_slope * lr
b1 = b1 - step_size
b1
loss_slope = -2 * np.sum(y - m * X.ravel() - b1)
step_size = loss_slope * lr
b2 = b1 - step_size
b2 | code |
105186076/cell_9 | [
"image_output_1.png"
] | from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X, y = make_regression(n_samples=4, n_features=1, n_informative=1, n_targets=1, noise=80, random_state=13)
from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit(X, y)
(reg.coef_, reg.intercept_)
y_pred = (78.35 * X + 0).reshape(4)
plt.scatter(X, y)
plt.plot(X, reg.predict(X), color='red', label='OLS')
plt.plot(X, y_pred, color='#ffa500', label='b(intersept) = 0-initial_random')
plt.legend()
plt.show() | code |
105186076/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit(X, y) | code |
105186076/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X, y = make_regression(n_samples=4, n_features=1, n_informative=1, n_targets=1, noise=80, random_state=13)
from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit(X, y)
(reg.coef_, reg.intercept_)
plt.scatter(X, y)
plt.plot(X, reg.predict(X), color='red')
plt.show() | code |
105186076/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
X.ravel()[:5]
m = 78.35
b1 = 100
loss_slope = -2 * np.sum(y - m * X.ravel() - b1)
lr = 0.1
step_size = loss_slope * lr
b1 = b1 - step_size
b1 | code |
105186076/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
X.ravel()[:5]
m = 78.35
b1 = 100
loss_slope = -2 * np.sum(y - m * X.ravel() - b1)
lr = 0.1
step_size = loss_slope * lr
b1 = b1 - step_size
b1
loss_slope = -2 * np.sum(y - m * X.ravel() - b1)
step_size = loss_slope * lr
b2 = b1 - step_size
b2
loss_slope = -2 * np.sum(y - m * X.ravel() - b2)
step_size = loss_slope * lr
b3 = b2 - step_size
b3 | code |
105186076/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
X, y = make_regression(n_samples=4, n_features=1, n_informative=1, n_targets=1, noise=80, random_state=13)
from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit(X, y)
(reg.coef_, reg.intercept_)
y_pred = (78.35 * X + 0).reshape(4)
X.ravel()[:5]
m = 78.35
b1 = 100
loss_slope = -2 * np.sum(y - m * X.ravel() - b1)
lr = 0.1
step_size = loss_slope * lr
b1 = b1 - step_size
b1
y_pred1 = (78.35 * X + b1).reshape(4)
loss_slope = -2 * np.sum(y - m * X.ravel() - b1)
step_size = loss_slope * lr
b2 = b1 - step_size
b2
y_pred2 = (78.35 * X + b2).reshape(4)
loss_slope = -2 * np.sum(y - m * X.ravel() - b2)
step_size = loss_slope * lr
b3 = b2 - step_size
b3
y_pred3 = (78.35 * X + b3).reshape(4)
plt.figure(figsize=(18, 6))
plt.scatter(X, y)
plt.plot(X, reg.predict(X), color='red', label='OLS', linewidth=3)
plt.plot(X, y_pred3, 'b--', label='b3 = {}_updated@step3'.format(b3))
plt.plot(X, y_pred2, color='#ffb347', label='b2 = {}_updated@step2'.format(b2))
plt.plot(X, y_pred1, color='#f8b878', label='b1 = {}_updated@step1'.format(b1))
plt.plot(X, y_pred, color='#ffa500', label='b = 0_initial_random')
plt.legend()
plt.show() | code |
105186076/cell_3 | [
"image_output_1.png"
] | from sklearn.datasets import make_regression
import matplotlib.pyplot as plt
X, y = make_regression(n_samples=4, n_features=1, n_informative=1, n_targets=1, noise=80, random_state=13)
plt.scatter(X, y)
plt.show() | code |
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